In addition to NAP projection and prior to training PLDA classifier, i-vectors were first centered by the mean calculated using the primary dataset and then length normalized [1]}. Based on our previous works on text-dependent speaker verification [2]}, [3]}, instead of using conventional Linear Discriminant Analysis (LDA), Regularized LDA (RLDA) [4]} was used. In this method, the within and between class covariance matrices are calculated using following formulas:
\(\mathbf {S}_w & = & \alpha \mathrm {\mathbf {I}} + \frac{1}{S}\sum _{s=1}^{S}\frac{1}{N_s}\sum _{n=1}^{N_s}(\mathbf {w_s}^n-\overline{\mathbf {w}_s})(\mathbf {w_s}^n-\overline{\mathbf {w}_s})^t\:, \\\mathbf {S}_b & = & \beta \mathrm {\mathbf {I}} + \frac{1}{S}\sum _{s=1}^{S}(\overline{\mathbf {w}_s} - \overline{\mathbf {w}})(\overline{\mathbf {w}_s} - \overline{\mathbf {w}})^t\:,\) 
